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Free Problems
Chapter 08 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 8: Overlaying Multiple Operations. The chapter explores advanced CUDA programming techniques including debugging with VS Code, using CUDA streams to overlap memory transfers and kernel execution, and working with multiple GPUs. These problems test your understanding of performance optimization strategies, debugging techniques, and parallel execution concepts in GPU programming.
27 pts Medium 97 cuda-streams parallel-execution gpu-hardware +12
Chapter 05 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 5 of GPU Programming with C++ and CUDA, focusing on GPU execution models, memory management, and performance optimization techniques. You'll explore threads, blocks, grids, asynchronous data transfers, streams, events, and shared memory concepts essential for high-performance GPU programming.
28 pts Medium 96 cuda thread-hierarchy blocks +7
Chapter 07 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 7 on CUDA Performance Strategies, including optimization techniques, profiling with NVIDIA Nsight Compute, shared memory usage, loop unrolling, fused instructions, and compiler configurations. These problems test your understanding of GPU performance optimization strategies discussed in the chapter.
30 pts Medium 97 gpu-optimization data-transfer performance +7
Chapter 06 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 6 on Parallel Algorithms with CUDA. The problems test understanding of parallel algorithm design principles, matrix operations, numerical integration, reduction operations, sorting algorithms, and synchronization concepts in GPU programming. Each question progresses from fundamental to advanced concepts to provide comprehensive practice with CUDA parallel algorithms.
24 pts Hard 97 amdahls-law parallel-limits speedup-calculation +7
Chapter 04 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 4 of GPU Programming with C++ and CUDA, focusing on parallel programming fundamentals, SIMD execution model, memory management, and practical GPU programming techniques. The problems progress from basic conceptual understanding to advanced implementation challenges, testing your comprehension of GPU parallelism, kernel execution, memory transfers, and performance optimization strategies discussed in the chapter.
24 pts Medium 99 simd parallel-programming gpu-architecture +7
Chapter 03 - GPU Programming with C++ and CUDA
This problem set covers fundamental CUDA programming concepts from Chapter 3, including kernel functions, thread hierarchy, device properties, and development environment setup. These questions test your understanding of GPU programming basics and CUDA execution model.
23 pts Medium 101 cuda-kernel gpu-programming parallel-execution +7
Premium Problems
Python I/O and Data Pipeline Assessment - Part 4
20 questions focused on PyTorch Dataset/DataLoader design: map/iterable datasets, transforms, custom collate/padding, worker seeding/sharding, num_workers/pin_memory/prefetch_factor, caching, memmap/shared memory, batching by size, profiling, and performance tuning.
10.00 60 pts Medium 98 torch.utils.data.dataset pytorch dataset +7
Chapter 02 - Numeric Python
This problem set covers key concepts from Chapter 2: Vectors, Matrices, and Multidimensional Arrays. The problems test understanding of NumPy array fundamentals, including array creation, indexing, slicing, operations, and vectorized computing. Each question is designed to reinforce the core concepts presented in the chapter.
5.00 26 pts Medium 97 numpy-arrays array-attributes shape +7
USAAIO 2025 R1P3 - Logistic Regression Implementation
This problem focuses on implementing logistic regression from scratch using the Titanic dataset. You will work through data pre-processing, mathematical derivations, and implement both gradient descent and Newton's method for logistic regression. The dataset contains passenger information from the Titanic, and your goal is to predict survival based on various features.
10.00 48 pts Easy 93 data-loading pandas data-exploration +7
USAAIO 2025 R1P2 - Basics of Neural Network - From Linear Regression to DNN Training
This problem is about the basics of neural network. Each part has its particular purpose to intentionally test you something. Do not attempt to find a shortcut to circumvent the rule. And all coding tasks shall run on CPUs, **not GPUs**.
10.00 36 pts Easy 96 learning-rate-scheduler pytorch optimization +12
USAAIO 2025 R1P1 - Fibonacci Matrix Form
Let us consider the following sequence: $$ F_n = F_{n-1} + F_{n-2},\ \forall\ n \ge 2. $$
8.00 27 pts Medium 96 fibonacci sequence linear algebra matrix form +7
IAIO 2024 Part 2 - Machine Learning Algorithms and Deep Learning
This problem covers the remaining categories of the 2024 International Artificial Intelligence Olympiad (IAIO), focusing on machine learning algorithms and deep learning. You'll work through practical implementations of k-means clustering, deep learning architectures, and advanced machine learning theory including kernel methods and the Perceptron algorithm. The problems cover: - K-means clustering algorithm implementation and convergence - Deep learning architectures (DALL-E, Transformers) - Perceptron algorithm and kernel methods - Mathematical proofs and theoretical analysis - Parameter counting and computational complexity
10.00 44 pts Hard 99 k-means clustering euclidean distance machine learning +7

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Featured PDFs

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Popular Topics

machine learning 56 deep learning 40 neural networks 35 reinforcement learning 33 system-design 28 grade5 27 optimization 14 large language models 13 attention mechanisms 13 combinatorics 13 system-architecture 13 natural language processing 12 aime problems 12 Number Sense 12 scalability 11 beginner 10 number theory 10 performance 10 transformers 9 capacity-planning 9
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